On Fri, 28 Jul 2000 10:38:18 -0700, jersey
<[EMAIL PROTECTED]> wrote:
> Background: I am an ecologist working with intertidal
> community structure data. My data is characterised by high
> non-normality and lack of equal variance between samples. I
> am currently exploring various non-parametric multivariate
> techniques, such as non metric multidimensional scaling, and
> the suite of statistic tests applicable to such a technique.
> (For those of you not familiar with nMDS, it is an
> ordination technique that arranges points in space that are
> determined from a similarity matrix derived from say, the
> Bray-Curtis method. In actuality, nMDS uses the RANK
> similarity, and thus eliminates the need for many
> assumptions about the original data set.
I read a question a few days ago from someone wanting to use an nMDS
procedure with N=119, whereas the program would not (reportedly)
accept N above 100. It was my impression that N=100 was a limit
imposed by computer memories in the early 1980s, which was when nMDS
was tried as a fad; and then people abandoned it and quit working on
the programs.
>
> My data is comprised of the following: percent cover data
> about seaweeds in the intertidal. This is determined by
> laying a 8 square quadrats on the beach (at 6 different
> sites - 8 quads at each), then sampling for a percent cover
> value of each species present. So, I have 8 replicate
> samples at each of six sites.
>
> Anyways, my question is the following: Is it preferable to
> POOL the 8 samples, or to average the samples? What are the
> pros and cons of each scenario? I'm aware there is a loss of
> power if you pool the samples, but does that make a
> difference in this situation.
>
> Any advice would be greatly appreciated.
>
I don't notice any hypothesis being mentioned, so it is impossible to
recommend pooling in order to test it. It is always possible to note
that the only numbers you can average (pool), if you want a good,
DESCRIPTIVE average, are ones that are rather similar. -- if the
differences are strong, we refer to the distinct groups.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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